Tracking and handling of super-hot data in non-volatile memory systems

ABSTRACT

A non-volatile memory organized into flash erasable blocks sorts units of data according to a temperature assigned to each unit of data, where a higher temperature indicates a higher probability that the unit of data will suffer subsequent rewrites due to garbage collection operations. The units of data either come from a host write or from a relocation operation. Among the units more likely to suffer subsequent rewrites, a smaller subset of data super-hot is determined. These super-hot data are then maintained in a dedicated portion of the memory, such as a resident binary zone in a memory system with both binary and MLC portions.

CROSS-REFERENCED APPLICATIONS

This application is a Continuation in part of U.S. patent application Ser. No. 13/468,737, filed May 10, 2012, which in turn claims priority from U.S. Provisional Application No. 61/487,244 filed May 17, 2011, which applications are incorporated in their entirety by this reference.

FIELD OF THE INVENTION

This application relates to the operation of re-programmable non-volatile memory systems such as semiconductor flash memory, and, more specifically, to efficient storing of data in block structures while minimizing rewrites.

BACKGROUND OF THE INVENTION

Solid-state memory capable of nonvolatile storage of charge, particularly in the form of EEPROM and flash EEPROM packaged as a small form factor card, has become the storage of choice in a variety of mobile and handheld devices, notably information appliances and consumer electronics products. Unlike RAM (random access memory) that is also solid-state memory, flash memory is non-volatile, and retaining its stored data even after power is turned off. Also, unlike ROM (read only memory), flash memory is rewritable similar to a disk storage device. In spite of the higher cost, flash memory is increasingly being used in mass storage applications. More recently, flash memory in the form of solid-state disks (“SSD”) is beginning to replace hard disks in portable computers as well as in fixed location installations. Conventional mass storage, based on rotating magnetic medium such as hard drives and floppy disks, is unsuitable for the mobile and handheld environment. This is because disk drives tend to be bulky, are prone to mechanical failure and have high latency and high power requirements. These undesirable attributes make disk-based storage impractical in most mobile and portable applications. On the other hand, flash memory, both embedded and in the form of a removable card or SSD are ideally suited in the mobile and handheld environment because of its small size, low power consumption, high speed and high reliability features.

Flash EEPROM is similar to EEPROM (electrically erasable and programmable read-only memory) in that it is a non-volatile memory that can be erased and have new data written or “programmed” into their memory cells. Both utilize a floating (unconnected) conductive gate, in a field effect transistor structure, positioned over a channel region in a semiconductor substrate, between source and drain regions. A control gate is then provided over the floating gate. The threshold voltage characteristic of the transistor is controlled by the amount of charge that is retained on the floating gate. That is, for a given level of charge on the floating gate, there is a corresponding voltage (threshold) that must be applied to the control gate before the transistor is turned “on” to permit conduction between its source and drain regions. In particular, flash memory such as Flash EEPROM allows entire blocks of memory cells to be erased at the same time.

The floating gate can hold a range of charges and therefore can be programmed to any threshold voltage level within a threshold voltage window. The size of the threshold voltage window is delimited by the minimum and maximum threshold levels of the device, which in turn correspond to the range of the charges that can be programmed onto the floating gate. The threshold window generally depends on the memory device's characteristics, operating conditions and history. Each distinct, resolvable threshold voltage level range within the window may, in principle, be used to designate a definite memory state of the cell.

Current commercial products configure each storage element of a flash EEPROM array to store either a single bit of data or more than a single bit of data. A single-level-cell (SLC) memory has each cell storing a single bit of data by operating in a binary mode, where a single reference level differentiates between two ranges of threshold levels of each storage element.

The threshold levels of transistors correspond to ranges of charge levels stored on their storage elements. In addition to shrinking the size of the memory arrays, the trend is to further increase the density of data storage of such memory arrays by storing more than one bit of data in each storage element transistor. A multi-level-cell (MLC) memory has each cell storing more a single bit of data by operating in a multi-level mode, where two or more reference levels differentiates between more than two ranges of threshold levels of each storage element. For example, commercial flash memory products now operate in four states (2 bits of data per storage element) or eight states (3 bits of data per storage element) or 16 states per storage element (4 bits of data per storage element). Each storage element memory transistor has a certain total range (window) of threshold voltages in which it may practically be operated, and that range is divided into the number of states defined for it plus margins between the states to allow for them to be clearly differentiated from one another. Obviously, the more bits a memory cell is configured to store, the smaller is the margin of error it has to operate in.

The transistor serving as a memory cell is typically programmed to a “programmed” state by one of two mechanisms. In “hot electron injection,” a high voltage applied to the drain accelerates electrons across the substrate channel region. At the same time a high voltage applied to the control gate pulls the hot electrons through a thin gate dielectric onto the floating gate. In “tunneling injection,” a high voltage is applied to the control gate relative to the substrate. In this way, electrons are pulled from the substrate to the intervening floating gate. While the term “program” has been used historically to describe writing to a memory by injecting electrons to an initially erased charge storage unit of the memory cell so as to alter the memory state, it has now been used interchangeable with more common terms such as “write” or “record.”

The memory device may be erased by a number of mechanisms. For EEPROM, a memory cell is electrically erasable, by applying a high voltage to the substrate relative to the control gate so as to induce electrons in the floating gate to tunnel through a thin oxide to the substrate channel region (i.e., Fowler-Nordheim tunneling.) Typically, the EEPROM is erasable byte by byte. For flash EEPROM, the memory is electrically erasable either all at once or one or more minimum erasable blocks at a time, where a minimum erasable block may consist of one or more sectors and each sector may store 512 bytes or more of data.

The memory device typically comprises one or more memory chips that may be mounted on a card. Each memory chip comprises an array of memory cells supported by peripheral circuits such as decoders and erase, write and read circuits. The more sophisticated memory devices also come with a controller that performs intelligent and higher level memory operations and interfacing. More recently, the memory devices in the form of SSD are being offered commercially in the form factor of a standard hard drive.

There are many commercially successful non-volatile solid-state memory devices being used today. These memory devices may be flash EEPROM or may employ other types of nonvolatile memory cells. Examples of flash memory and systems and methods of manufacturing them are given in U.S. Pat. Nos. 5,070,032, 5,095,344, 5,315,541, 5,343,063, and 5,661,053, 5,313,421 and 6,222,762. In particular, flash memory devices with NAND string structures are described in U.S. Pat. Nos. 5,570,315, 5,903,495, 6,046,935.

Nonvolatile memory devices are also manufactured from memory cells with a dielectric layer for storing charge. Instead of the conductive floating gate elements described earlier, a dielectric layer is used. Such memory devices utilizing dielectric storage element have been described by Eitan et al., “NROM: A Novel Localized Trapping, 2-Bit Nonvolatile Memory Cell,” IEEE Electron Device Letters, vol. 21, no. 11, November 2000, pp. 543-545. An ONO dielectric layer extends across the channel between source and drain diffusions. The charge for one data bit is localized in the dielectric layer adjacent to the drain, and the charge for the other data bit is localized in the dielectric layer adjacent to the source. For example, U.S. Pat. Nos. 5,768,192 and 6,011,725 disclose a nonvolatile memory cell having a trapping dielectric sandwiched between two silicon dioxide layers. Multi-state data storage is implemented by separately reading the binary states of the spatially separated charge storage regions within the dielectric.

Flash Memory Characteristics and Trends

Flash memory behaves quite differently from traditional disk storage or RAM. First, existing data stored in the flash memory cannot be updated by simply being overwritten. Each cell must first be erased before a new write can take place on it. Consequently the update is always written to a new free location. To improve performance, a group of cells are operated on in parallel to access data page by page. When a page of data is updated by having the updated page written to a new location, the superseded page is rendered invalid and obsolete and becomes garbage cluttering the storage and will eventually be cleaned out to free up the space it is occupying.

Managing the updates and discarding the invalid ones are complicated by the block structure of flash memory. It is relatively time consuming to erase flash memory and to improve erase performance, the memory is organized into erase blocks where a whole block of memory cells are erased together simultaneously. A block generally contains a number of pages. As data is stored in a block page by page, eventually some of that data becomes obsolete. This means the block will contain many garbage data taking up space. However, the block can only be erased as a unit and so before the garbage data can be erased with the block, the valid data in the block must first be salvaged and copied into another block. This operation is commonly referred to as garbage collection and is an overhead of the block structure of the flash memory. The larger the block, the more time is required for the garbage collection. Similarly, the more frequently the data in the block is being updated, the more frequently will the block need to be garbage collect. Garbage collection is preferably performed in the foreground like during a write operation. This obviously will degrade the write speed.

Early applications of flash memory have been mainly for storing media files such as music and video files for portable hosts. These files tend to be a long run of data of sequential logical addresses which fills up the memory block by block. These data are archival in nature and not subject to much updating. Thus, the block structure works well for these type of data and there is little performance hit during writing since there is seldom need for garbage collection. The orderly sequential-address nature of the data allows logical address range to be partitioned into logical groups, with each logical group aligned with an erase block in the sense that the data of a logical group will fit neatly in a block. In this way, the addressing granularity is mainly at the block level as a page with a given logical address can be located by which block is storing the logical group it belongs to. Since the logical group is stored in the block in a self-indexed manner with its logical addresses in sequential order, the page can be quickly located.

The block management system implementing logical groups typically deals with updates and non-sequential writes by tracking them at the page level. It budgets a predetermined amount of resource for the page level tracking which manifests has limiting the number of logical groups having non-sequential or obsolete data. Generally, when subject to updates, some of the orderly blocks will contain obsolete data and keeping track of them will also consume part of the resource. When over the budget, a selected block with non-sequential or obsolete data is restored back to an orderly block in sequential order. This is accomplished by rewriting into a new block in sequential order with the latest updates. However the relocation will exact a performance hit. Such a system will work well if a host writes data that are conducive to maintaining mostly such orderly blocks being tracked at the block level, with only some random writes being tracked at the page level. Thus, by implementing logical groups aligned to block boundary, the address table is greatly simplified and reduced.

However, the block management system implementing logical groups will begin to be less optimized if the host writes mostly short and non-sequential data. This type of write pattern is prevalent in applications from a personal computer or smart mobile device. Solid-state disk (SSD) using flash memory is an attractive replacement for disk storage due to its low power, speed and ruggedness. Instead of long sequential writes, the flash memory must now deal mostly with short random writes. Initially, the performance will not suffer since as long as free space can be found, the data can be written there. However, with constant use and frequent updates, the predetermined resource for page tracking will eventually be exhausted. At that point, performance can take a big hit as the next write may have to be accompanied by a relocation of a block. The larger is the block the longer it will take to perform relocation of a block. Also a large block and short and non-sequential data will cause the logical group in the block to contain invalid data more frequently and consume page addressing resource faster and therefore cause relocation to take place more frequently.

The problem with the large block size cannot be easily solved by simply reducing the block size as the block size tend to increase geometrically with each new generation of memory technology. With higher integration of circuits more memory cells are being fitted in the same die. The block size, measure in columns and rows increases geometrically. This is especially the case for memory of the NAND type. The memory is an array of NAND strings where each string is a daisy chain of memory cells and a minimum erase block must be formed by a row of such NAND string. If the NAND string has 32 cells, a block will contain 32 rows of cells. The number of memory cells in a NAND string also increases with each generation, so the block size increases column-wise and row-wise.

The block size, which is dictated by the physical memory structure, is in present generation as large as 4 MB. On the other hand, the operating system of personal computers typically allocates logical sectors in size of 512 kB and often writes a page as a cluster of logical sectors in 4 kB unit. Thus, there is a great mismatch in the addressing granularity of a logical group corresponding to a block and a page. In the scheme of logical group, the ideal situation for a block is either nothing is written or the block is filled up sequentially with the entire logical group of valid data. In either case there is no fragmentation and there is no need for garbage collection or relocation. In the case of short random writes into a large block, the block becomes non-ideal very quickly and eventually will need relocation. This amounts to inefficient writes since the same page may have to be written and then re-copied one or more times (also referred to as “write amplification”.)

An alternative, conventional addressing approach suitable for short random writes is to not use logical groups, but to track every page independently as it is being written to a block. Instead of maintaining the stored data as orderly logical group in a block, each page is tracked as to which block it is stored in and at what offset in the block. Thus, in this page addressing scheme, there is no burden of storing or maintaining data in groups in order of sequential logical addresses. However, the page addressing scheme will have an address table much larger than that for the logical group address scheme. For example, if there are 1000 pages in a block, then the address table for the page addressing scheme will be approximately 2 to 3 orders of magnitude larger.

The page addressing scheme exact penalty in terms of a much larger address table. In practice, it will require more system resources and a relative large RAM to work with the memory controller. This is because the address table is usually maintained in flash memory but is cached to the controller RAM during operation to provide faster access. Current technology allows at most 2 to 4 MB of RAM to be fabricated on the controller chip. This is insufficient for systems using a page addressing scheme and additional external RAM chips will be required. The additional pinouts and interface circuits to support external RAM chips would add significantly to the cost.

Another problem with addressing granularity having very small units, such as 4 kB, is that it creates fragmented data, which is scattered between the blocks so much that maximum parallelism during read and data copy (due to update) is not achievable. Also, the amount of copy increases as small update can still trigger copy of one or more entire block.

Thus, there is a need to provide a nonvolatile memory that can efficiently handle data access characterized by short random writes into large blocks without suffering from the disadvantages and problems mentioned above.

SUMMARY OF THE INVENTION

A method of operating on units of data in a non-volatile memory system is presented, where the memory system having a memory circuit organized into blocks of non-volatile memory cells that are erasable together. The method includes determining from among the units of data a set of less than all of the units of data that are more likely to suffer subsequent rewrites due to garbage collection. The method determines a smaller subset of the units of data from among the units of data of said set that yet more likely to suffer subsequent rewrites due to garbage collection. The units of data of said subset are then maintained in a dedicated portion of the array.

The foregoing features may be implemented individually or together in various combinations, depending upon the specific application. Additional aspects, advantages and features of the scrubbing system herein are included in the following description of exemplary examples thereof, which description should be taken in conjunction with the accompanying drawings. All patents, patent applications, articles and other publications referenced herein are hereby incorporated herein by this reference in their entirety for all purposes.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a host in communication with a memory device in which the features of the present invention are embodied.

FIG. 2 illustrates a page of memory cells, organized for example in the NAND configuration, being sensed or programmed in parallel.

FIG. 3 illustrates schematically an example of a memory array organized in erasable blocks.

FIG. 4 illustrates schematically a memory chip having multiple arrays and operations for maximum parallelism.

FIG. 5 illustrates schematically, a memory structure having higher degree of parallelism.

FIG. 6 illustrates a binary memory having a population of cells with each cell being in one of two possible states.

FIG. 7 illustrates a multi-state memory having a population of cells with each cell being in one of eight possible states.

FIG. 8 illustrates an example of a physical memory architecture suitable for practicing the invention.

FIG. 9 illustrates schematically the data path between the SLC portion and the MLC portion in a 2-layer data storage system.

FIG. 10 illustrates in more detail the SLC layer shown in FIG. 9.

FIG. 11 illustrates a page in the memory organization of the block management system according to the present invention.

FIG. 12 illustrates a logical group in the block management system.

FIG. 13A illustrates an erase block accommodating data from multiple logical groups.

FIG. 13B is a flow diagram illustrating the scheme of storing host writes to the non-volatile memory in terms of small logical groups.

FIG. 14 illustrates a system architecture for managing the blocks and pages across the different memory partitions according to the present invention.

FIG. 15 illustrates in more details the second layer shown in FIG. 14.

FIG. 16 illustrates the ‘temperature’ sorting of the logical groups for the ‘hot’ logical group case.

FIG. 17 illustrates the ‘temperature’ sorting of the logical groups for the ‘cold’ logical group case.

FIG. 18 illustrates how different types of writes are sorted into block streams according to their perceived temperature interactively.

FIG. 19 is a flow diagram illustrating the scheme of temperature sorting for memory storage and operations.

FIG. 20 is a flow diagram illustrating the scheme of temperature sorting at the logical group level.

FIG. 21 is a flow diagram illustrating the scheme of temperature sorting at the block level.

FIG. 22 illustrates a system architecture for managing super-host data across the different memory partitions.

FIGS. 23-26 are flow diagrams illustrating an exemplary embodiment for the handling of super-hot data.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Memory System

FIG. 1 illustrates a host in communication with a memory device in which the features of the present invention are embodied. The host 80 typically sends data to be stored at the memory device 90 or retrieves data by reading the memory device 90. The memory device 90 includes one or more memory chip 100 managed by a memory controller 102. The memory chip 100 includes a memory array 200 of memory cells with each cell capable of being configured as a multi-level cell (“MLC”) for storing multiple bits of data, as well as capable of being configured as a single-level cell (“SLC”) for storing 1 bit of data. The memory chip also includes peripheral circuits 204 such as row and column decoders, sense modules, data latches and I/O circuits. An on-chip control circuitry 110 controls low-level memory operations of each chip. The control circuitry 110 is an on-chip controller that cooperates with the peripheral circuits to perform memory operations on the memory array 200. The control circuitry 110 typically includes a state machine 112 to provide chip level control of memory operations via a data bus 231 and control and address bus 111.

In many implementations, the host 80 communicates and interacts with the memory chip 100 via the memory controller 102. The controller 102 co-operates with the memory chip and controls and manages higher level memory operations. A firmware 60 provides codes to implement the functions of the controller 102. An error correction code (“ECC”) processor 62 processes ECC during operations of the memory device.

For example, in a host write, the host 10 sends data to be written to the memory array 100 in logical sectors allocated from a file system of the host's operating system. A memory block management system implemented in the controller stages the sectors and maps and stores them to the physical structure of the memory array. A preferred block management system is disclosed in United States Patent Application Publication Number: US-2010-0172180-A1, the entire disclosure of which is incorporated herein by reference.

Physical Memory Architecture

In order to improve read and program performance, multiple charge storage elements or memory transistors in an array are read or programmed in parallel. Thus, a “page” of memory elements are read or programmed together. In existing memory architectures, a row typically contains several interleaved pages or it may constitute one page. All memory elements of a page will be read or programmed together.

FIG. 2 illustrates a page of memory cells, organized for example in the NAND configuration, being sensed or programmed in parallel. FIG. 2 essentially shows a bank of NAND strings 50 in the memory array 200 of FIG. 1. A “page” such as the page 60, is a group of memory cells enabled to be sensed or programmed in parallel. This is accomplished in the peripheral circuits by a corresponding page of sense amplifiers 210. The sensed results are latches in a corresponding set of data latches 220. Each sense amplifier can be coupled to a NAND string, such as NAND string 50 via a bit line 36. For example, the page 60 is along a row and is sensed by a sensing voltage applied to the control gates of the cells of the page connected in common to the word line WL3. Along each column, each cell such as cell 10 is accessible by a sense amplifier via a bit line 36. Data in the data latches 220 are toggled in from or out to the memory controller 102 via a data I/O bus 231.

The page referred to above is a physical page memory cells or sense amplifiers. Depending on context, in the case where each cell is storing multi-bit data, each physical page has multiple data pages.

The NAND string 50 is a series of memory transistors 10 daisy-chained by their sources and drains to faun a source terminal and a drain terminal respective at its two ends. A pair of select transistors S1, S2 controls the memory transistors chain's connection to the external via the NAND string's source terminal and drain terminal respectively. In a memory array, when the source select transistor S1 is turned on, the source terminal is coupled to a source line 34. Similarly, when the drain select transistor S2 is turned on, the drain terminal of the NAND string is coupled to a bit line 36 of the memory array. Each memory transistor 10 in the chain acts as a memory cell. It has a charge storage element 20 to store a given amount of charge so as to represent an intended memory state. A control gate of each memory transistor allows control over read and write operations. The control gates of corresponding memory transistors of a row of NAND string are all connected to the same word line (such as WL0, WL1, . . . ) Similarly, a control gate of each of the select transistors S1, S2 (accessed via select lines SGS and SGD respectively) provides control access to the NAND string via its source terminal and drain terminal respectively.

Erase Blocks

One important difference between flash memory and other type of memory is that a cell must be programmed from the erased state. That is the floating gate must first be emptied of charge. Programming then adds a desired amount of charge back to the floating gate. It does not support removing a portion of the charge from the floating to go from a more programmed state to a lesser one. This means that update data cannot overwrite existing one and must be written to a previous unwritten location.

Furthermore erasing is to empty all the charges from the floating gate and generally takes appreciably time. For that reason, it will be cumbersome and very slow to erase cell by cell or even page by page. In practice, the array of memory cells is divided into a large number of blocks of memory cells. As is common for flash EEPROM systems, the block is the unit of erase. That is, each block contains the minimum number of memory cells that are erased together.

FIG. 3 illustrates schematically an example of a memory array organized in erasable blocks. Programming of charge storage memory devices can only result in adding more charge to its charge storage elements. Therefore, prior to a program operation, existing charge in charge storage element of a memory cell must be removed (or erased). A non-volatile memory such as EEPROM is referred to as a “Flash” EEPROM when an entire array of cells 200, or significant groups of cells of the array, is electrically erased together (i.e., in a flash). Once erased, the group of cells can then be reprogrammed. The group of cells erasable together may consist of one or more addressable erase unit 300. The erase unit or block 300 typically stores one or more pages of data, the page being a minimum unit of programming and reading, although more than one page may be programmed or read in a single operation. Each page typically stores one or more sectors of data, the size of the sector being defined by the host system. An example is a sector of 512 bytes of user data, following a standard established with magnetic disk drives, plus some number of bytes of overhead information about the user data and/or the block in with it is stored.

In the example shown in FIG. 3, individual memory cells in the memory array 200 are accessible by word lines 42 such as WL0-WLy and bit lines 36 such as BL0-BLx. The memory is organized into erase blocks, such as erase blocks 0, 1, . . . m. If the NAND string 50 (see FIG. 2) contains 16 memory cells, then the first bank of NAND strings in the array will be accessible by select lines 44 and word lines 42 such as WL0 to WL15. The erase block 0 is organized to have all the memory cells of the first bank of NAND strings erased together. In memory architecture, more than one bank of NAND strings may be erased together.

Increased Parallelism with Metapage and Metablock Organization

FIG. 4 illustrates schematically a memory chip having multiple arrays and operations for maximum parallelism. For example, the memory chip is fabricated with two dies, DIE 1 and DIE 2. Each die contains two memory planes. For example, DIE 1 contains memory plane 1 and memory plane 2, and DIE 2 contains memory plane 3 and memory plane 4. Each memory plane contains multiple blocks and each block contains multiple pages. For example, memory plane 1 includes Block 1 which in turn includes a page P1.

The blocks such as Block 1-Block 4 are each minimum erase units (MEUs) fixed by the physical architecture of the memory array in a memory plane, such as the block 300 shown in FIG. 3. Similarly, the pages such as P1-P4 are each minimum Read/Write units fixed by the number read/write circuits that operate in parallel.

In order to maximize programming speed and erase speed, parallelism is exploited as much as possible by arranging for multiple pages of information, located in multiple MEUs, to be programmed in parallel, and for multiple MEUs to be erased in parallel.

FIG. 5 illustrates schematically, a memory structure having higher degree of parallelism. For example, pages P1-P4 are linked together as a “metapage”, which at the system level, is operated on as a minimum unit of read or write. Similarly, Block 1-Block 4 are linked together as a “metablock”, which at the system level, is operated on as a minimum erase unit. The physical address space of the flash memory is treated as a set of metablocks, with a metablock being the minimum unit of erasure. Within this specification, the terms “metablock”, e.g., 300-4 and “block” 300 are used synonymously to define the minimum unit of erasure at the system level for media management, and the term “minimum erase unit” or MEU is used to denote the minimum unit of erasure of flash memory. Similarly, the terms “metapage”, e.g., 60-4 and “page” 60 are used synonymously with the understanding that a page can be configured into a metapage at the system level with a higher degree of parallelism.

While FIG. 4 illustrates that higher degree of parallelism can be achieve by aggregating memory structures from multiple planes in a memory chip, it should be understood that in another embodiment, the planes may be distributed among more than one memory chip.

The linking and re-linking of MEUs into metablocks is also disclosed in United States Patent Publication No. US-2005-0144516-A1 and U.S. Pat. No. 7,139,864, the entire disclosure of these two publications are hereby incorporated herein by reference.

Examples of Binary (SLC) and Multi-Level (MLC) Memory Cells

As described earlier, an example of nonvolatile memory is formed from an array of field-effect transistors, each having a charge storage layer between its channel region and its control gate. The charge storage layer or unit can store a range of charges, giving rise to a range of threshold voltages for each field-effect transistor. The range of possible threshold voltages spans a threshold window. When the threshold window is partitioned into multiple sub-ranges or zones of threshold voltages, each resolvable zone is used to represent a different memory states for a memory cell. The multiple memory states can be coded by one or more binary bits.

FIG. 6 illustrates a binary memory having a population of cells with each cell being in one of two possible states. Each memory cell has its threshold window partitioned by a single demarcation level into two distinct zones. As shown in FIG. 6(0), during read, a read demarcation level rV₁, between a lower zone and an upper zone, is used to determine to which zone the threshold level of the cell lies. The cell is in an “erased” state if its threshold is located in the lower zone and is in a “programmed” state if its threshold is located in the upper zone. FIG. 6(1) illustrates the memory initially has all its cells in the “erased” state. FIG. 6(2) illustrates some of cells being programmed to the “programmed” state. A 1-bit or binary code is used to code the memory states. For example, the bit value “1” represents the “erased” state and “0” represents the “programmed” state. Typically programming is performed by application of one or more programming voltage pulse. After each pulse, the cell is sensed to verify if the threshold has moved beyond a verify demarcation level vV₁. A memory with such memory cell partitioning is referred to as “binary” memory or Single-level Cell (“SLC”) memory. It will be seen that a binary or SLC memory operates with a wide margin of error as the entire threshold window is only occupied by two zones.

FIG. 7 illustrates a multi-state memory having a population of cells with each cell being in one of eight possible states. Each memory cell has its threshold window partitioned by at least seven demarcation levels into eight distinct zones. As shown in FIG. 7(0), during read, read demarcation levels rV₁ to rV₇ are used to determine to which zone the threshold level of the cell lies. The cell is in an “erased” state if its threshold is located in the lowest zone and is in one of multiple “programmed” states if its threshold is located in the upper zones. FIG. 7(1) illustrates the memory initially has all its cells in the “erased” state. FIG. 7(2) illustrates some of cells being programmed to the “programmed” state. A 3-bit code having lower, middle and upper bits can be used to represent each of the eight memory states. For example, the “0”, “1”, “2”, “3”, “4”, “5”, “6” and “7” states are respectively represented by “111”, “011”, “001”, “101”, “100”, “000”, “010” and “110”. Typically programming is performed by application of one or more programming voltage pulses. After each pulse, the cell is sensed to verify if the threshold has moved beyond a reference which is one of verify demarcation levels vV₁. to vV₇. A memory with such memory cell partitioning is referred to as “multi-state” memory or Multi-level Cell (“MLC”) memory. In a number programming method employs multiple programming passes before the cells are programmed to their target states in order to alleviate floating-gate to floating-gate perturbations.

Similarly, a memory storing 4-bit code will have lower, first middle, second middle and upper bits, representing each of the sixteen states. The threshold window will be demarcated by at least 15 demarcation levels into sixteen distinct zones.

As the memory's finite threshold window is partitioned into more regions, the resolution for programming and reading will necessarily become finer. Thus, a multi-state or MLC memory necessarily operates with a narrower margin of error compared to that of a memory with less partitioned zones. In other words, the error rate increases with the number of bits stored in each cell. In general, error rate increases with the number of partitioned zones in the threshold window.

Endurance is another problem with flash memory that limits its life of use. With every program/erase cycling, some tunneling electrons are trapped in the dielectric between the floating gate and the channel region that results in the narrowing of the threshold window. This will eventually result in program and read errors. Since MLC memory has lower tolerance for error, it also has less endurance compared to SLC memory.

Memory Partitioned into SLC and MLC portions

FIG. 8 illustrates an example of a physical memory architecture suitable for practicing the invention. The array of memory cells 200 (see FIG. 1) is partitioned into a first portion 410 and a second portion 420. The second portion 420 has the memory cells configured as high density storage with each cell storing multiple bits of data. The first portion 410 has the memory cells configured as lower density storage with each cell storing less number of bits than that of the second portion. For example, memory cells in the first portion 410 are configured as SLC memory to store 1 bit of data each. Memory cells in the second portion 420 are configured as MLC memory to store 2 bits of data each. The first portion storing 1 bit of data per cell will also be referred as D1 and the second portion storing 2 bit of data per cell as D2. In view of the discussion earlier, the first portion will operate with more speed, a much wider margin of error and more endurance compared to that of the second portion.

A memory partitioned into two portions such as into D1 (1-bit) and D3 (3-bit) portions is disclosed in U.S. application Ser. No. 12/642,584 filed on Dec. 18, 2009, the entire disclosure of which is incorporated herein by reference.

FIG. 9 illustrates schematically the data path between the SLC portion and the MLC portion in a 2-layer data storage system. The first layer is the main input buffer for incoming data and operates on the SLC portion 410 of a NAND memory which is faster/higher-endurance/higher-cost memory compared to the MLC portion 420. The second layer is the main data archive storage and operates on the MLC portion which is slower/lower-endurance/lower-cost memory.

The main operations in such system are labeled in FIG. 9 are as follows:

1. Host data or control data write to SLC portion

2. Data copy within SLC portion to reclaim partially obsolete SLC block, aka ‘compaction’

3. Host data direct write to MLC portion, usually used for long sequential writes

4. Data move from SLC to MLC portion, aka ‘folding’

5. Data copy within MLC portion for MLC block reclaim, aka ‘MLC compaction’

The above structure can be built with many other additional features, mainly related to the use of different addressing schemes and addressable data unit granularity.

FIG. 10 illustrates in more detail the SLC layer shown in FIG. 9. The typical structure of SLC layer (see diagram above) uses multiple blocks, usually one Write/Update block data and one Relocation/Compaction block for data copied during block reclaim (or, they can be combined). The following main rules usually apply:

1. Blocks are linked in the chain according to the order in which they were programmed.

2. The least recently programmed block is selected as the SLC move/folding block, from which data may be moved/folded to the MLC write block.

3. The block with the lowest volume of valid data is selected as the SLC reclaim block, from which valid data is relocated to the SLC relocation block connecting to the head of the chain.

4. An SLC move block or SLC relocation block is added to the SLC empty block list on completion of a data move/folding or block reclaim operation.

In addition to that, the two-layer structure can be in fact more than two layer, if there are more types of memory, say RAM, or 3rd type of NVM.

Also, in the each ‘memory’ layer, there might be multiple sub-systems, with different data handling, which also referred to as ‘layer’.

The prior art systems based on NAND memory usually have the following storage hierarchy. The SLC partition has SLC blocks to implement a Binary Cache and Binary Update blocks.

The Binary Cache is used for some or all data. Data is stored in the Binary Cache with fine granularity of 1 or 8 (4 KB) sectors. Typically, the Binary Cache is used to cache small and random fragments of a page. It is then evicted to the Binary Update block.

The Binary Update blocks map most of the data in units of Logical Group. Each Logical Group has a size that corresponds to the SLC block. So, one Binary block can store up to one Logical Group in which the pages are in sequential order of logical address. This layer does not exist in cluster-based systems, as in those systems all Binary blocks are used as Binary Cache.

The MLC partition has MLC blocks for storing the data in higher density than the SLC blocks. Typically, data is stored MLC-block by MLC-block. Thus in a memory with D1 and D3 partitions, 3 SLC blocks is folded (relocated) to 1 MLC block.

Eviction of data from the Binary Cache to the SLC update blocks and to the MLC blocks is based on Least-Recently-Written basis. The problem in all systems that most of the data (exception is data updated while in binary Cache) is going to SLC blocks first so that it works pretty much as a FIFO buffer. Then all data go to MLC blocks. In both SLC and MLC portions, the data can be copied many times due to padding (to make a full addressing unit), or to compact blocks and reclaim obsolete space. The Stress Factor (aka Write Amplification) is high and applies to both SLC and MLC block partitions. The data in SLC is also allocated in MLC (double allocation), which increases required number of blocks in the system due to double-budgeting.

Generally in prior art systems, the main approach is to use finer granularity units, which assume high-end processing and large RAM requirements, adding extra cost and power consumption.

Also, very small unit, such as 4 KB, creates a problem of the data being fragmented, scattered between the blocks so much that maximum parallelism during read and data copy (due to update) is not achievable. Also, amount of copy increases as small update can trigger copy of an entire block(s).

Block Management System Using Small Logical Groups with Selective Distribution Across Memory Partitions Based on Activity

Small Logical Groups

The invention has an architecture which addresses the above problems, in particular the undesirable FIFO buffer behavior of SLC blocks which increases write amplification; the fragmentation of data, which reduces parallelism; the high intensity of processing, which requires large RAM and high power; the duplicate capacity budget for data in SLC blocks, which is inefficient and wasteful.

According to one aspect of the invention, a nonvolatile memory is provided with a block management system in which an ordered logical address range from a host is partitioned into logical groups where a block stores multiple logical groups of data. Each logical group is of a size having a range from at least the same order of magnitude to an order of magnitude higher as the size of a host write but at least of a size of a page or metapage which is a unit of read or write of maximum parallelism supported by the memory. By having the size of the logical group decoupled from that of the erase block, and being of a size more compatible with the size and nature of host writes, the logical group provides the benefit of simplifying addressing and conserving limited system resource while not triggering excessive rewrites which impact performance.

The implementation of logical groups of smaller size has the benefit of not triggering excessive rewrites while at the same time allowing a smaller address table to be used. This has the benefit of the address table being of sufficiently compact size to be cached in RAM integrated on a controller chip without the need for costly external RAM.

FIG. 11 illustrates a page in the memory organization of the block management system according to the present invention. Essentially, a host writes units of data which are identified by their logical address, LBA (logical block address). The memory operates on a logical page 62 of data in parallel. The page 62 can hold data for a number of LBAs. For example, each page holds data from M units of LBAs and a page, Page(LP₀), may be filled with data from LBA₀ to LBA_(M-1). Depending on the memory architecture a page is at least a group of cells/data that can be serviced by a corresponding group of read/write circuits in a memory plane. In the preferred embodiment, the page is a metapage as described in connection with FIG. 5 to achieve maximum parallelism. For example, the metapage is of size 32 kB to 64 kB. With a host write cluster of 4 kB, a metapage can hold 8 to 16 clusters.

FIG. 12 illustrates a logical group in the block management system. For simplicity of addressing, instead of tracking each page 62 independent, a group of pages is tracked as one unit. Essentially, the logical addressed space of the host system is partitioned into logical groups 350, each group being a subset of the logical address space defined by a range of LBAs or logical page numbers. For example, logical group LG0 is constituted from N logical pages with logical page nos. LP₀ to LP_(N-1) and the next logical group LG1 is constituted from N logical pages with logical page nos. LP_(N) to LP_(2N-1), etc.

A logical group 350 is stored in the memory with its logical page numbers in sequential order so that the pages in it are self-indexed. In this way, addressing for the pages 62 in the logical group is by simply keeping track at the logical group level instead of the page level. However, with updates of pages in a logical group, garbage collection needs to be performed to reclaim space occupied by invalid pages. In prior art systems, the logical group has a size that aligns with the size of an erase block. In this way, garbage collection on an erase block is simply to salvage the valid data of the logical group and rewrite the entire logical group to a new block.

FIG. 13A illustrates an erase block accommodating data from multiple logical groups. Unlike, prior art systems, the size of the logical group 3350 is decoupled from that of the erase block and is not the same size as the erase block. The logical group 350 is down-sized to be more compatible with the size and nature of host writes. A block 310 (which preferable is a metablock) in the SLC portion 410 is able to accommodate data for P number of logical groups. For example, the SLC block stores the following logical groups: LG0, LG1, LG2, LG1′, . . . , etc where LG1′ is an updated version of LG1.

By using logical groups, addressing is less intense and places less demand on system resources without requiring an expensive off-chip RAM to work with the memory controller.

FIG. 13B is a flow diagram illustrating the scheme of storing host writes to the non-volatile memory in terms of small logical groups.

However, as erase block size is increasing with every generation of flash memory, prior art approach of aligning a logical group with a block results in a system that is not optimized for short and random host writes. This type of host write patterns are prevalent in applications under desktop and laptop computers and smart mobile devices. These data patterns, characterized by frequency updates and non-sequential writes, tend to cause more frequent rewrites of the memory in order to maintain the logical group sequential order. In other words, the prior logical group size causes a great deal of write amplification and degrade performance and wear out the memory prematurely.

Thus, each logical group is down-sized to a range from at least the same order of magnitude to an order of magnitude higher as the size of a unit of host write but at least of a size of a metapage which is a unit of read or write of maximum parallelism supported by the memory. This will be optimized for data patterns that are frequently updated or non-sequential and not to trigger excessive rewrites. For example, a logical group may have 4 metapages. If the metapage holds 8 to 16 host clusters, then a logical group may hold 32 to 64 clusters. At the same time, the logical group size may be judicially increased as a tradeoff for the purposed of relieving demand on addressing resource so that the controller chip need not operate with external RAM.

FIG. 13B is a flow diagram illustrating the scheme of storing host writes to the non-volatile memory in terms of small logical groups.

STEP 500: Organizing the non-volatile memory into blocks of memory cells that are erasable as a unit, each block for storing a plurality of pages, each page for accessing a predetermined number logical unit of data in parallel, each logical unit having a logical address assigned by the host. STEP 510: Defining a plurality of logical groups by partitioning a logical address space of the host into non-overlapping sub-ranges of ordered logical addresses, each logical group having a predetermined size within delimited by a minimum size of at least one page and a maximum size of fitting at least two logical groups in a block. STEP 520: Buffering individual host writes. STEP 530: Staging the individual host writes logical group by logical group. STEP 540: Storing any staged logical groups into the non-volatile memory. STEP 550: Done.

In a preferred implementation, the memory is partitioned in SLC and MLC portions and comprises, first, second and third operational and functional layers. The first and second layers operate in the SLC portion. The third layer operates in the MLC portion. The first layer is for initially storing write data from a host and staging the data logical-group by logical-group before relocating each logical group into either the second or third layer. The second layer provides active storage in a pool of SLC blocks for storing host data at the logical-group level. When the pool is full, more room is made by evicting the logical groups with the least potential rewrites to the third layer which stores at a higher density.

In this way an active set of user data is preferentially maintained in the faster SLC memory and only when capacity runs out in the SLC memory will selected logical groups more suited for storage in the MLC memory be evicted thereto

FIG. 14 illustrates a system architecture for managing the blocks and pages across the different memory partitions according to the present invention. The blocks and pages in the memory arrays are managed by a block management system, which resides as firmware 60 in the memory controller 102 (see FIG. 1).

The memory is partitioned into a SLC portion 410 and a MLC portion 420. The block management system implements a first, fragment caching layer 412, a second, logical group sorting layer 414 and a third, cold logical group archiving layer 422. These are operational and functional layers. The first two layers 412 and 414 operate in the SLC portion 410 and the third layer 421 operates in the MLC portion 420.

The first, fragment caching layer 412 operates on binary blocks 310 of the SLC portion 410 and is for initially storing data from a host and staging the metapages logical-group by logical-group before relocating each logical group into the MLC portion 420. The staging is to gather the data into entire logical groups. The gathering could be from fragments of a host write or by padding in combination with existing data already stored in the non-volatile memory. The SLC portion 410 includes two structures: a resident binary zone 402 and a binary cache 404. The Binary Cache 404 is storage for mainly short fragments with fine addressing unit (sector), where the data can be moved/evicted to SLC blocks 310 or MLC blocks 320. The resident binary zone 402 is reserved for known frequently updated areas with short updates, typically NTFS or other File System tables data only.

The second, logical group sorting layer 414 stores data logical-group by logical-group in a pool of SLC update/storage blocks 310. The writes to this pool come from host writes or from rewrites due to garbage collection. If the host data is mainly of short fragment, it is first cached in the first layer 412 before being evicted from the first layer to the second layer 414. If the host data is less fragmented (medium size), where complete logical group can be had, it is written directly to the second layer 414.

Essentially, the second layer 414 provides a fast SLC storage area where fragmented and medium size host writes land. Unlike prior systems, where there is no second layer and the first layer 412 essentially acts as a FIFO to transit data to the third layer 422 in the MLC portion 420 before the data can be accessed, this second layer 414 maintains a working set of user data in the fast SLC portion 410.

Thus, a user will experience high performance writes as the pool of SLC update/storage blocks are being filled. Only when the pool is full will the system move some logical groups over to the third layer (MLC) to make room.

Hot/Cold Logical Group Sorting

A non-volatile memory organized into flash erasable blocks sorts units of data according to a temperature assigned to each unit of data, where a higher temperature indicates a higher probability that the unit of data will suffer subsequent rewrites due to garbage collection operations. The units of data either come from a host write or from a relocation operation. The data are sorted either for storing into different storage portions, such as SLC and MLC, or into different operating streams, depending on their temperatures. In general, the temperature sorting technique is operable in. SLC as well as MLC portions. This allows data of similar temperature to be dealt with in a manner appropriate for its temperature in order to minimize rewrites. Examples of a unit of data include a logical group and a block.

In a preferred implementation, the memory is partitioned in SLC and MLC portions and comprises, first, second and third operational and functional layers. The first and second layers operate in the SLC portion. The third layer operates in the MLC portion. The first layer is for initially storing write data from a host and staging the data logical-group by logical-group before relocating each logical group into either the second or third layer. The second layer provides active storage in a pool of SLC blocks for storing host data at the logical-group level. When the pool is full, more room is made by evicting the logical groups with the least potential rewrites to the third layer which stores at a higher density.

Each logical group in the second layer is ranked by its potential for future rewrites due to garbage collection. A temperature from a finite range is assigned to each logical group with the coldest logical group first to be evicted to the third layer. Ranking criteria include the rate of update the logical group is experiencing and the length of time the logical group is between updates. Logical groups relocated from the second memory layer to the third memory layer will be accessed at the third memory layer. Logical group remaining at the second memory layer will be accessed directly at the second memory layer.

FIG. 15 illustrates in more details the second layer shown in FIG. 14. A pool of binary blocks 310 is provided for storing the logical groups. As each block 310 is filled and some of the logical groups in it are updated, the block will need to be garbage-collected. Valid logical groups in the block are relocated to a new block. The logical groups in the pool are sorted according to its ‘temperature’.

The logical group to be moved to the third layer 422 is selected according to its ‘temperature’. The second layer 414 also provides facilities for ranking and sorting the logical groups by how likely they need rewrites. A logical group is considered ‘hot’ when it contains data that is frequently updated and is from short and random host writes because the logical group will need more rewrites due to more garbage collections. Conversely, a logical group is considered ‘cold’ when it contains data that is seldom updated and is long sequential host writes because the logical group will remain relatively static requiring little or no rewrites. One ‘temperature’ ranking criterion is the rate of update the logical group is experiencing.

Thus, whenever the SLC block pool in the second layer 414 is full, the logical groups with the coldest temperature are preferentially evicted to the MLC pool in the third layer 422.

Logical groups relocated from the second layer 414 to the third layer 422 will be accessed at the third layer 422. Logical groups remaining at the second layer 414 will continued to be accessed at the second layer 414.

The sorting and distinguishing of the actively updated and less actively updated logical groups are significant when the first 412 and second 414 layers operate in a SLC memory portion 410 and the third layer 422 operates in the MLC portion 420. By keeping the active logical groups in the SLC memory as a working set and only move the inactive ones to the MLC memory, rewrites of the logical group whenever there are updates to it are minimized in the MLC memory. This in turn minimizes the total number of rewrites a logical group will suffer.

The third layer 422 stores at a higher density (MLC) the coldest logical groups evicted from the second layer. This process is also referred to as ‘folding’ SLC data to MLC data.

The sorting of hot and cold logical groups and retaining the hotter logical groups in the second layer allows users to access these potentially performance-impacted data in the faster and more enduring SLC memory.

While the sorting scheme has been described with respect to sorting at the logical group level, it is to be understood that the invention is equally applicable to sorting at the level of other data units, such as sorting at the fragment level or sorting at the block level.

According to prior art systems, eviction of data from Binary Cache to SLC update blocks and to MLC blocks are based on Least Recently Written basis, applied on the block level. This means that it is actually based on Least Recently Programmed block, regardless of the fact how long ago the data was programmed by the host (the block can be programmed recently due to Compaction, but contain old and cold data.)

Also, eviction is often based (especially in Binary Cache) on operation efficiency criteria, with focus on increasing effect of individual operation, say Logical Group eviction yields most empty space.

The problem in all cases above is that they do not take into account the host update pattern, such as frequency of updates, and even how long ago the data was written. As a result, data which is likely to be accessed soon, may be archived.

U.S. Pat. No. 7,633,799 discloses usage of different data access pattern criteria such as LRU, hit rate by write and read commands. But, the prior art does not teach specific practical methods of making it work in a data storage system, such as making the choice efficient and at the same time avoid excessive processing, RAM and control update requirements.

The approach in the present invention is to aim for minimizing Write Amplification. Write amplification is caused by a future write elsewhere in the system. Write amplification is caused by co-location of active (hot) and inactive (cold) data being mixed in a physical block. Whenever, there is a mixing of hot and cold data in a block, the data in the block will eventually need to be relocated or rewritten to another block. As blocks get larger, it becomes more challenging to keep active and inactive regions co-located.

The invention provides a collection of practical methods to sort data in a way to detect the best data to evict/archive to the next layer of storage. The methods mainly use known principles, specifically they are based on analyzing access pattern and history. The focus is on making the data sorting practical.

The main methods include:

1. Rank the relative activeness of addressable data units (Logical Groups) by assigning a ‘temperature’ value to individual fragments. The Temperature value can be stored with the data itself or in a separate table, or alongside with addressing entries. The temperature values themselves can be based on:

a) Least Recently Written (by the host) criteria for the data fragments/units;

b) Recent Hit (access, e.g., read) rate;

c) Data fragment length (the shorter the data is, the more likely it is to be hit soon);

d) Number of block compactions copies for the data as an indicator of data age;

e) Combination of a) and b) and c), which produces the best results.

2. Provide a temperature value ‘reduction’. For example, when measured over time, the hit rate may drop, which translates to a reduction in temperature. This allows a finite range of useful temperature to be defined and makes the use of the method practical. The temperature is reduced by the following methods:

a) Working within a limited dynamic range of temperature, (say 0=very cold, 7=very hot, in 3-bit temperature case) biasing the temperature to not go beyond the 0 values for extreme cold cases or saturating the temperature to not go beyond 7 at the extreme hot cases. In other word, all extreme cases have the same values, 0 or 7, after some point.

b) Leveling values of temperature values for fragments/units to avoid extreme saturation of values and loss of accuracy. In other word, using the limited dynamic range in a region of maximum effectiveness.

3. Using block-level temperature criteria, where the ‘temperature’ is tracked on a block level rather than on a fragment or Logical Group level. Two main cases include:

a) Tracking ‘temperature’ explicitly;

b) Implicit tracking by sorting blocks in the block list by data age or by degree of hotness/coldness.

In one embodiment, the temperature sorting is at the logical group level. The coldest logical group will be the first to be evicted from the second layer to the third layer. The criteria for a logical group to be evicted include the following.

1. Time stamps (TS). The temperature is determined as a time stamp value of the logical group. A time stamp indicates when the logical group was last written. The longer it was last written, the colder is the temperature. Practically using a limited TS range, very old logical groups beyond a maximum TS value will all be considered to have the same coldest temperature.

The advantage of TS is that it has the fastest response to access pattern change. The disadvantage is that it provides no previous history.

An example of using time stamp is to provide an 11-bit time stamp for each logical group in the binary block pool of the second layer. When a logical group is written to the pool, one option is to assign an initial time stamp value of 0 (bias=0). This may be suitable when the data written is long sequential data. Another option is to have a value of X (bias=X). This may be suitable for data of unknown type and X can be set to middle of the time stamp range. Every time there is a write of a logical group into the pool the time stamp of the logical group being written is set to the initial value and the time stamps of the existing logical groups in the pool are incremented by one. The time stamp for a logical group does not change during compaction. In this way, the time stamp provides a relative measure of how recently written is each of the logical groups in the pool.

2. Basic Write Counts. The temperature is determined as a write count of the logical group. A write count indicates how many times the logical group was written or the frequency of updates. For example, at a new update of the logical group, the write count is incremented. The advantage of write count is that it keeps history information. The disadvantage is that it may make old hot logical groups ‘sticky’.

3. Temperature as a function of time stamp and write count. The initial temperature value of X is between 0 and Max when the logical group is first written. The value is incremented if the logical group is written again (as in write count), so the method adds bias to logical groups that are written more times recently. The value is decremented as the average value for all logical groups is going up by one (as approximate MSB of time stamp).

An example of assigning a 3-bit temperature as a function of time stamp and write count is as follows:

When the logical group is written, it has a temperature of X between 0 and Max (7). If the logical group is written again the temperature is incremented by one (as in a write count). The temperature is decremented under the following situation:

1. When the average temperature for all logical groups is going up by 1 as this can saturate on the top. This serves to level the population;

2. When there are no enough logical groups LGT=0 to evict;

3. When the average is going above a threshold (say MAX/2);

4. To level the ratio between 0s and MAXs

Every time a Logical Group is updated by the host and is written to either Binary Cache or one of Update Blocks (upon completion of the previously written Logical Group in the same Update Block) is it assigned the following value of LGT:

-   -   Any Logical Group written to Sequential Stream gets assigned the         lowest LGT value of 0.     -   If Logical Group is in the Active Binary set (addressed by         Master Index), except of sequential write back-to-back by short         writes without address jump, LGT value is incremented by 1 or         set to Highest Cold LGT=3, whichever is the highest. The LGT         value cannot exceed the Highest Cold value of 7.     -   If Logical Group is in not the Active Binary set (not addressed         by Master Index), initial LGT value of Highest Cold LGT=3 is         assigned.

When a Logical Group is written to one of Relocation Blocks it is assigned the default LGT value of Lowest Cold=0.

When a Logical Group is evicted from Binary Cache to one of Relocation Blocks it is assigned the LGT value of Highest Cold=3.

Only Logical Group with LGT=0 can be evicted and folded to MLC block. If there are no enough Logical Groups to be folded, all LGTs are to be decremented.

FIG. 16 illustrates the ‘temperature’ sorting of the logical groups for the ‘hot’ logical group case. LG temperature is a combined function of update frequency and age. The Active Binary Working Set (ABWS) is the pool of SLC blocks in the second layer. It represents the short list of Hot Logical Groups and blocks, where the LGT (Logical Group Temperature) values are being tracked.

Sorting is done on the basis of LGT (Logical Group Temperature) values for the Logical Groups. LGT values are stored for limited number of Logical Groups currently addressed by master index, making Active Binary set. The master index is a table that lists all the logical groups in the SLC pool of the second layer. Each LGT is 3 bit in size and has a range from 0 (coldest) to 7 (hottest).

All Logical Groups in ALL Streams are subject to Sorting, but only Logical Groups written to Binary blocks (Update Blocks, Relocation blocks, or stored in Closed Blocks) in the Active Binary Set (those currently addressed by Master Index) are being sorted at the given time and LGT values are stored in Master index.

Logical Groups addressed via GAT (Binary Blocks in Inactive Binary Set and MLC blocks) are considered equally ‘very cold’ and by default are considered having lowest LGT value of 0. GAT is a lookup table that keeps track of the mapping between logical groups and blocks.

Initially, a given logical group that resides in an MLC block is updated. The temperature for this logical group therefore goes up from zero. As it is unclear how active this logical group will become in the near future, it is assigned a middle value temperature, with LGT=3. As it transpires, the logical group is soon updated another 5 times. With each update hit, the temperature LGT is incremented by one, which brings it to a maximum value of LGT=7. Thereafter, there were no further updates on the logical group and so LGT remains at LGT=7. At this point, it turns out that the binary pool is full and a set of logical groups with LGT=0 is evicted (folded) to the MLC layer. The departure of the set of logical groups raised the average temperature of the pool and therefore the temperature of all remaining logical groups in the SLC pool is decremented by one, so that the given logical group now has LGT=6. After a while with no updates to the given logical group, there is another folding, which will decrement the given logical group's LGT to 5. At this point, the given logical group has a high temperature and will continue to ‘live’ in the SLC pool.

FIG. 17 illustrates the ‘temperature’ sorting of the logical groups for the ‘cold’ logical group case. In this case, a logical group residing in the third, MLC layer is updated and returned to the binary pool in second, SLC layer. After sitting in binary pool without further updates, the temperature cools down back to LGT=0. When the pool is full and needs to evict some logical groups, the given logical group is folded back to the third, MLC layer.

In another embodiment, the sorting can be performed at the block level. This is an alternative approach if there are too many logical groups in the pool to individually track their temperature. Instead, the temperature is tracked at the block level where all logical groups in a block are treated as if they have the same temperature. The sorting options is this case include the following:

Same time stamp for logical groups in the same Binary block (explicit Block level TS)—to model

-   -   Each Binary block has TS same for all logical groups written for         the block.     -   Sort hot and cold data by blocks     -   TS=Current block TS. Current Block TS increments after each new         data Update block closure.     -   During compaction TS is approximated on the basis of TSs in the         source blocks     -   For example, the time stamp TS is 8 bits (compacted TS=greatest         TS of the first compaction source) or could be 6 bits (track         average TS for compaction blocks).     -   Can bias cold data, (TS=Current TS−bias), but not at the bottom,         options are: bias=0 or bias =X.     -   Hot-Cold data Binary block sorting (implicit implementation of         the Block level TS)—no need to model     -   Each Binary block is listed in the UB info in time allocation         order for new data update blocks. Equivalent to TS being the         same for all logical groups written for the block.     -   During compaction, the new block's position in the list is         chosen approximately according to the source block locations. In         other words, the new block has approximately the same         temperature as the source block.     -   During compaction TS is approximated on the basis of TSs in the         source blocks     -   Logical groups from the block at the end of the list get evicted     -   The advantages are that it has no extra records, no overflow, no         increments etc. Also it is very good for Binary Cache where         there is no single table record, but multiple BCIs (binary cache         indices), which are impossible to update all together. The         disadvantage is that it requires data copies to re-sort block         records.

The principles described above apply to a system with two or more layers of data storage, which can be non-volatile or mixed. The same rules can be applied to a specific type of storage in one of the layers, say Binary Cache sub-system or Update Blocks.

Advantage of this solution is that system performance impact is minimized and there is no increase in controller RAM space.

Block Streams to Separate Hot/Cold Data by LGT

In another embodiment, units of data are sorted according to their temperatures into different block streams such that the blocks in each operating stream only involves data of similar temperature. The goal is to separate hot data from cold data as soon as possible and at every opportunity. The hot data and cold data have different obsolescence and garbage collection/relocation schedules. For example, hot data will become obsolete faster and require more frequent garbage collection/rewrites. When the cold data are not mixed in with the hot data, it will not incur unnecessary rewrites. Most likely, the hot data will obsolete itself without triggering relocation of cold data from one block to another block, and the cold data in cold blocks will stay there without compactions/relocations due to the hot data.

One example is the host writes entering the pool of binary blocks in the second layer are sorted into different block streams as soon as possible. Another example is the data unit coming from a relocation operation.

FIG. 18 illustrates how different types of writes are sorted into block streams according to their perceived temperature interactively. The sorting applies to the source at the second layer with incoming data and also applies to data moved by compaction to separate hot/cold blocks.

Generally, within a memory partition, there can be different type of data streams generated by different sources as shown. The data writes in each of the different types of data streams has its own update frequencies and randomness that could be sorted by a temperature described earlier.

In the binary block pool, the blocks are designated as either a ‘hot’ block for storing logical group with LGT>3 or a ‘cold’ block with LGT=<3. The temperature is determined on the fly after observing the write pattern. For example, when a logical group is written into the binary block pool for the first time, its temperature is unknown and therefore assigned a neutral temperate of LGT=3 (between 0 and 7, as the 3-bit example before). The logical group is written to a block designated to be cool. If the next write is an update of the logical group, the stream is deemed to be hot and the updated logical group is written to a different binary block for storing hot logical groups.

On the other hand, if the successive writes are sequential, the stream is deemed cold and the successive logical groups are all written to the cold binary block containing the first write.

If the successive writes are sequential and the trend continues for a predetermined period, the stream is deemed a series of long sequential writes and is directed to be folded to the MLC portion either directly or via the binary block pool. In the direct case, the stream is in a by-pass mode as soon as it is identified. The head of the sequential stream marooned in a cold or even hot block will eventually be relocated.

The different data streams described above can be created by a user and therefore come from a user logical partition. Some of the write streams in the partition may also be created from relocation operations.

Partitions

In general, different logical partitions such as user partition, OS (operating system) partition and ‘sticky’ binary partition may be maintained, each with its own mix of different type of data streams, some with predetermined temperature. For example, in the OS partition, the system data are known to be fragmented and fast changing, so there is not even the need to determine the temperature. It is simply assigned a hot temperature and stored in the hot blocks. The same is true for the ‘sticky’ partition where the data there are meant to stay in the binary SLC portion. Thus its data stream is always ‘hot’ and is stored in the hot blocks.

Separate by LBA data to partition—meaning that a block does not have data coming from different partitions. The assumption is that data in different partitions is written by different applications (say OS in one, and user in another) and those writes often do not interleave. Say OS can write many commands, then user write many, but there is not a lot of interleave. By separating the writes from the different partitions to different blocks, compaction/relocation of, say, user data, triggered by OS writes, and vice versa, will be reduced.

Blocks and logical groups are subject to sorting by LGT without partition boundaries. That means that it is not necessary to budget a number of Closed blocks per partition, and the blocks are distributed on demand. For example, if the OS partition is active and the user partition is not, then up to all Closed update blocks can be allocated to the OS partition as all user partition's logical groups will be sorted to cold state and folded to the MLC portion.

Support for Multiple Update Blocks Per Stream

Writes from a steam may be stored into multiple blocks. Every time a first logical group is partially written in a first block and is followed by a write of a different, second logical group, the second logical group is written to a second block in the hope that subsequent writes will furnish the incomplete data to complete the first logical group. This will reduce fragmentation. Up to a predetermined number of update blocks can be opened contemporaneously for this purpose. Beyond that, the incomplete logical group is made complete by padding the incomplete data.

FIG. 19 is a flow diagram illustrating the scheme of temperature sorting for memory storage and operations.

STEP 600: Organizing the non-volatile memory into blocks of memory cells that are erasable together.

STEP 610: Ranking each unit of data by assigning a temperature, where a higher temperature indicates a higher probability that the unit of data will suffer subsequent rewrites due to garbage collection operations.

STEP 620: Performing an operation on the unit of data in a manner dependent on the temperature of the unit of data.

STEP 630: Done.

FIG. 20 is a flow diagram illustrating the scheme of temperature sorting at the logical group level.

STEP 700: Organizing the non-volatile memory into blocks of memory cells that are erasable together.

STEP 710: Partitioning the non-volatile memory into a SLC portion and an MLC portion, where memory cells in the SLC portion each stores one bit of data and memory cells in the MLC portion each stores more than one bit of data.

STEP 720: Providing a plurality of logical groups by partitioning a logical address space of the host into non-overlapping sub-ranges of ordered logical addresses, the logical groups having a size that multiple logical groups fit in a block.

STEP 730: Storing data logical group by logical group in each block of the SLC portion.

STEP 740: Ranking each logical group stored in the SLC portion by a temperature, where a higher temperature indicates a higher probability the logical group will suffer subsequent rewrites due to garbage collection operations.

STEP 750: In response to a demand to free up room in the SLC portion, preferentially relocating a logical group with the coldest temperature from the SLC portion to the MLC portion.

STEP 760: Done.

FIG. 21 is a flow diagram illustrating the scheme of temperature sorting at the block level.

STEP 800: Organizing the non-volatile memory into blocks of memory cells that are erasable together.

STEP 810: Partitioning the non-volatile memory into a SLC portion and an MLC portion, where memory cells in the SLC portion each stores one bit of data and memory cells in the MLC portion each stores more than one bit of data.

STEP 820: Ranking each block in the SLC portion by a temperature, where a higher temperature indicates a higher probability the block will suffer subsequent rewrites due to garbage collection operations.

STEP 830: In response to a demand to free up room in the SLC portion, preferentially relocating data in a block with the coldest temperature from the SLC portion to the MLC portion.

STEP 840: Done.

Super-Hot Data Tracking and Handling

This section considers the detection and handling of narrow ranges of super-hot data. The previous sections, that are developed further in U.S. patent application Ser. Nos. 13/468,720 and 13/468,737, that can be considered complimentary to this section in that the techniques of this section can be used together with them or separately.

More specifically, most memory systems are designed and optimized according to certain “usage models” and behaviors. For example, in the arrangement illustrated above with respect to FIG. 14, the binary cache 404 is used to absorb most of the random writes and results in lessening the amount of write amplification or stress factor for the MLC blocks 320. The present section addresses the special case of “super-hot” LBAs, such as would be written by journaling writes of the host file system (such as NTFS in Win OS, and EXT3 in Android). Previous mechanisms are based on the handling of relatively wide hot LBA ranges, which handle the super-hot writes relatively ineffectively due to the extreme locality and frequency of such writes, typically with an LBA footprint of just few unique KBytes and a frequency that may exceed other hot writes by 2 or 3 orders of magnitude. Further, the LBA locations of this super-hot data are often not static and can vary due to file system format parameters and also move over the device's life. Such extreme write patterns can cause a mixture of hot data, now divided to super-hot and non-super-hot, where the super-hot writes can make a mixed memory block partially obsolete almost immediately, due to own updates; and then, this in turn triggers an almost immediate block compaction and increases the corresponding write amplification factor.

Various mechanisms have been introduced for dealing with the hot data, but not directed at the more extreme sort of situation under consideration here. For instance, U.S. Pat. No. 7,509,471 describes the use of an adaptive system zone with a dynamic allocation of the hottest data to special handling blocks. For the arrangements described in the preceding sections, a Resident Binary Zone (RBZ, 404 FIGS. 14 and 15) is a fixed range Binary Cache Zone, which may be focused on a relatively narrow and hot LBA area handling, but in a static arrangement that is focused on the case of known, well-defined location of the hot data (such as a file access, or FAT, table). With respect to the hot/cold sorting arrangement of the preceding section, this is most efficient for the tracking of dynamically changing hot spots, but is based on managing relatively large LBA areas, without separating out super-hot narrow locations. One way in which super-hot areas can be detected is by analyzing the file system data, which may work well for a specific, known system type and configuration, but is often not flexible enough solution by nature and requires customization.

FIG. 22 again illustrates a system architecture as in FIGS. 14 and 15, but with the additional data paths of this section included. Specifically, the Fragment Caching Layer 412 again includes a binary cache section 404 and a Resident Binary Zone (RBZ) 402, but the RBZ 402 is not dynamic, rather than a static range RBZ. Under this arrangement, the RBZ 402 will not just be used for previous, permanent resident binary data, but also for data which is determined by the system to qualify as super-hot. This super-hot data will have a sort of conditional residency, going to the RBZ 402 when determined to be hot enough and being evicted if it cools sufficiently, space is needed for yet hotter data, or combination of these. In the exemplary embodiment, a path from the Binary Cache 404 to the RBZ 402 for when hot data qualifies as super-hot, is transferred to the RBZ 402 and will subsequently be written to the RBZ 402. Data evicted from RBZ 402 goes to an update block UB in layer 414. Other embodiments can promote data from, and demote data to, other locations, but in the exemplary embodiment, the system dynamically promotes super-hot data within the fragment caching layer 412, from the binary cache 404 to the RBZ 402, but with eviction from both the RBZ 402 and the binary cache 404 going into update blocks or the LG sorting layer 414. More generally, cooled data that is evicted from the RBZ 402 can be sent to SLC update blocks (UB) in the LG sorting layer 414, MLC Update Blocks, back to binary cache 404, or any combination of the other areas outside of the RBZ.

For a static version of RBZ 402, the RBZ is used to map certain Logical Groups that are known ahead of time, so that the RBZ does not have data for other Logical Groups. The groups sent to the static RBZ could, for example, be defined during formatting or set by analyzing file system data, processes that require knowledge of a host's behavior. The binary cache 404 is used for any data, acting as a cache and keeping only a limited amount of data, with the excess being evicted to the main storage blocks. Under that static arrangement, the RBZ does not have an eviction process, the residency being permanent. For the dynamic RBZ 402 presented in this section, the memory system itself allows for the detection of super-hot as a function of data access pattern without the need to understand host access patterns, so that it is universally applicable: when super-hot data is detected the system can move the entire Logical Group to the RBZ. The benefit of this dynamic arrangement is achieved by two things in particular: one is to eliminate (or at least minimize) the mixing super-hot data with the other data, so that compactions triggered by super-hot data do not include copies of non-super-hot data. The other is that super-hot compactions can be made very efficient due to small size: if the RBZ's useful capacity is a small fraction of RBZ gross capacity, then any compaction's overhead is relatively very small. The exemplary embodiment moves entire the LG to the RBZ, as this can be a relatively simple arrangement. In other embodiments, only partial logical groups could be moved. It is usually preferable, though, that the RBZ is selected for only a small range of logical block addresses, with enough physical space set aside to avoid excess eviction and the mixing-up of the super-hot data with other data.

The techniques of this section allow the memory system to detect the narrow range of super-hot data by hot/cold sorting over a wider range of data, so that the extreme temperature range can be accurately detected and adjusted. The selected narrow range is then mapped to the RBZ 402, where the write can be handled with minimal write amplification. There are several aspects involved in this technique. A first of these is a method of tracking data “temperature” for a wide range of N logical units in order to detect the a smaller number M of the hottest (M<N) in a longer run of data, and then handle them in a fragment storage zone having a low write amplification and with a range change only if a longer term move is detected.

With respect to the detection of longer term moves, this could come in where the system may, say, track 32 LGs but only store the hottest 4 in the RBZ, when there may be a significant amount of hysteresis in the process of replacing an older LG group in the RBZ with a newer LG. A newer super-hot logical group should be host for a relatively prolonged period of time before it is elevated to the RBZ. This sort of arrangement also allows for the system to not store the temperature values non-volatile memory, but only in RAM, as no logical group can quickly qualify as a new-super hot LG even if the system were to lose all previous temperature values, since those logical groups already in the RBZ are already set by a high threshold and not readily replaced.

The written logical block address ranges can be sorted by various host write pattern parameters. This temperature can be based on:

-   -   a) Relative age of the data, using a least recently updated         principle;     -   b) Recent frequency of updates, as multiple updates are more         likely to repeat, even if interleave with the other writes;     -   e) Write length, where the focus is on short writes; or     -   d) Combination of above.         In most cases, option d), a combination of two or more of a),         b), and c) will provide the best results. Optionally, in some         embodiments, to this list can be added:     -   e) Maintaining consistency over relatively long operating         periods.         This option will discussed more with respect to FIG. 26. The         idea of e) is to detect and discount localized hits on logical         groups that are part of shorter term drift patterns.

Another aspects is a method for storing the “temperature” value in RAM in order to minimise control write overhead, and restart the tracking after power cycle without loss of focus on the hottest range. For both this and the other aspects, it should be noted that these are not based on host data analysis or the use host issued hints, which can be beneficial in many cases.

The exemplary is again based on the architecture FIG. 22, which takes the previously static range of the RBZ and makes it dynamic. FIGS. 23-25 outline an exemplary algorithm for tracking and storing the hottest M LG fragments within an RBZ cache. The Logical Group temperature being tracked here by the RBZ is not the same LGT used in the previous section by the master index for Active Binary Working Set (ABWS) purposes. Both the LG temperature used in the exemplary algorithm here and the temperature of the previous section are across the same range (3 bits), although differing ranges can be used for either of these. The exemplary algorithm of this section tracks the N hottest LGs and stores M (M<N) LGs within the RBZ. Any LG fragments evicted from the RBZ get evicted to Update Blocks.

FIGS. 23-25 are a flow for an exemplary embodiment for dealing with super-hot data. The flow picks up the process at 901, when logical group (LG) fragment is sent to the binary cache by the firmware, with FIG. 23 giving the detail for when the logical group already has fragments in the RBZ. The LG is checked at 903 to see whether or not it is already found in the resident binary zone. If so, at 905 the logical group's temperature is checked to see whether it already at the maximum value and, if so, the fragment is written to the RBZ at 907.

If the temperature is not at the maximum value, its value is increased (incremented by 1, for example) at 909 and subsequently checked again at 911 to see whether the temperature is now at the maximum value. (With respect to 909, also see the discussion of FIG. 26 below.) If 911 finds that the LG temperature is now at the maximum temperature, at 913 all of the other RBZ and tracked groups' temperatures are decremented and the fragment is written to the RBZ at 907. If, at 911, the logical group temperature is not equal to the maximum, the temperature is checked against an intermediate value for the temperature, here 3, at 915: if not less than the intermediate value, the fragment is written to the RBZ at 907; if it is less than the intermediate value, the temperature is first set to the intermediate value at 917 before the fragment is written in at 907.

Going back to 903, if the logical group is not already in the RBZ, 919 checks on whether the logical group's temperature is being tracked. FIG. 24 continues the flow for when the LG temperature is already being tracked in RBZ. FIG. 25 continues the flow for case when the LG temperature is not already being tracked in RBZ.

FIG. 24 starts with the “Yes” path out of 919, corresponding to when the logical groups has no fragments in the RBZ, but is already being tracked for logical group temperature in the RBZ, and begins with this temperature being incremented at 921. (With respect to 921, also see the discussion of FIG. 26 below.) At 923, the temperature is checked to see whether it is greater than the maximum and, if so, it is set to the maximum before going on to 927. At 927, the temperature is checked against an intermediate value (here, 3 is again used) and if the LG temperature is less than 3, it is set at 3 in 929 before going on to 931. (As with many of the other step, the order of 923/925 could be switched with 927/929 depending on the embodiment.)

After adjusting the logical group's temperature, at 931 it is checked against the maximum value (7 of 0-7 in the exemplary embodiment) and if the temperature is at the maximum, at 933 all of the other RBS and tracked logical group temperatures are decremented before checking, at 935, to see whether the system has the maximum number of logical groups already stored in the RBZ. If the RBZ is not at the maximum number of stored LGs, then the fragment is written to the RBS at 937.

If either the system has the maximum number of LGs stored in the RBZ (at 935) or the LG temperature is not at the maximum value (at 931), the flow goes on to 939. At 939 the system checks whether or not the lowest temperature in the other stored logical groups is less the logical group temperature of the currently considered logical group decremented by some amount. Here, LGT-3 is used. If not, the fragment is written to the normal binary cache (404 of FIG. 22) at 941; if so, the logical group with the lowest stored temperature is evicted from the RBZ at 943, with the fragment then being written in to the RBZ at 945. As discussed above, in the exemplary embodiment, evicted logical groups are sent to update blocks.

FIG. 25 picks up the “No” path from 919, corresponding to the case when the logical group is not being tracked by the RBZ cache. At 947, the system checks on whether the number of logical groups being tracked is less than the maximum number and, if so, the logical group and temperature are stored in the tracking pool and given an intermediate temperature of, in the exemplary embodiment, 3. The fragment is then written into the (normal) binary cache 953, but will now be tracked. Returning to 947, if instead the number of logical groups being tracked is at the maximum number allowed, then at 949 the logical group with the lowest temperature is evicted from tracking and the RBZ if stored in it. When evicted at 949, the data and logical group can go to various locations outside of the RBZ (Binary Cache, SLC UB, MCL Update Block), depending on the embodiment. For any of these cases, it is often preferable to continue tracking it as if it were one of the super-hot data candidates, although even if an embodiment were to remove it from tracking where evicted at 949, the LG would go back as soon again as it is hit by a host write.

FIG. 26 relates to the optional detection and discounting of localized hits on LGs that are part of only a fairly short term drift pattern and could lead to inconsistencies in the flow of FIGS. 23-25. In many applications, it is preferable to favor the RBZ housing long term super-hot data over short term logical groups which may appear hotter. For example, a spike of a high number of hits on a particular LG group should not displace the LGs in the RBZ when the spike is of short duration. When such a spike does not last for enough time, it cannot be taken as an indicator for such behavior being consistent into the future. FIG. 26 is an example of a solution to reduce the effects of such short repetition spikes in a write pattern.

FIG. 26 begins when the “avoid localized write” feature is available by adjusting 909 (FIG. 23), 921 (FIG. 24), or both by switching these to a call to FIG. 26. At 961 it is determined whether or not the “avoid localized write” feature is enabled: if not, at 963, the temperature of the logical group is incremented at 963; if so, at 965 it is checked whether this same logical group was hit last. If it was not hit last time (“No” from 965), the temperature is again incremented at 963, while if it was, the logical group temperature is left the same at 967. FIG. 26 is based at 965 on just the immediately preceding hit, but other embodiments can use more involved criteria.

The process represented in FIGS. 23-26 is just one example for implementing the process. For example, the steps can be done in differing orders than in the example, as noted above. The example also used a 3-bit temperature value, giving a 0-7 range, and the same value of 3 for all instances of an intermediate temperature, but other values can be used. Also, the exemplary embodiment is arranged so that the “cooling” process is slow, to help avoid that non-typical long writes for logical groups that have been promoted to the RBZ lead to these logical groups being overly easily re-assigned out of the RBZ. For any of these embodiments, this arrangement can help with the optimization of the writing of super-hot data, such as the writes associated with journaling in Android EXT and Windows's NTFS.

Conclusion

Although the various aspects of the present invention have been described with respect to specific embodiments, it will be understood that the invention is protected within the full scope of the appended claims. 

It is claimed:
 1. A method operating on units of data in a non-volatile memory system including a memory circuit having an array organized into blocks of non-volatile memory cells that are erasable together, comprising: providing a plurality of logical groups by partitioning a logical address space of the host into non-overlapping sub-ranges of ordered logical addresses, wherein each unit of data is a logical group; determining from among the units of data a set of less than all of the units of data that are more likely to suffer subsequent rewrites due to garbage collection; determining a smaller subset of the units of data from among the units of data of said se! that yet more likely to suffer subsequent rewrites due to garbage collection, and maintaining the units of data of said subset in a dedicated portion of the array, wherein determining whether a unit of data is more likely to suffer subsequent rewrites due to garbage collection includes assigning a temperature, where a higher temperature indicates a higher probability that the unit of data will suffer subsequent rewrites due to garbage collection operations; wherein determining from among the units of data a set of less than all of the units of data that are more likely to suffer subsequent rewrites due to garbage collection includes tracking the temperature of a plurality of N logical groups; and wherein determining a smaller subset of the units of data from among the units of data of said set that yet more likely to suffer subsequent rewrites due to garbage collection includes determining a plurality of the N hottest logical groups from among the N logical groups, where M and N are integers and M is less than N.
 2. The method of claim 1, further comprising: storing the temperature in a volatile random access memory used by control circuitry of the memory system.
 3. The memory system of claim 2, wherein the memory system restarts assigning and tracking temperatures subsequent to a power cycle.
 4. A method of operating on units of data in a non-volatile memory system including a memory circuit having an array organized into blocks of non-volatile memory cells that are erasable together, comprising: determining from among the units of data a set of less than all of the units of data that are more likely to suffer subsequent rewrites due to garbage collection; determining a smaller subset of the units of data from among the units of data of said set that yet more likely to suffer subsequent rewrites due to garbage collection; and maintaining the units of data of said subset in a dedicated portion of the array, wherein determining whether a unit of data is more likely to suffer subsequent rewrites due to garbage collection includes assigning a temperature, where a higher temperature indicates a higher probability that the unit of data will suffer subsequent rewrites due to garbage collection operations, wherein the method further comprises storing the temperature in a volatile random access memory used by control circuitry of the memory system, wherein the memory system restarts assigning and tracking temperatures subsequent to a power cycle, and wherein when the memory system restarts assigning and tracking temperatures subsequent to a power cycle, units of data are assigned a default value.
 5. The method of claim 4, wherein said default values are biased towards units of data already in the dedicated portion.
 6. The method of claim 4, wherein temperature is a multi-bit digital value.
 7. The method of claim 4, wherein assigning a temperature includes: ranking a unit of data as having a lower temperature when the unit of data is less recently written by a host.
 8. The method of claim 4, wherein assigning a temperature includes: ranking a unit of data as having a higher temperature when the unit of data is has a higher recent frequency of updates.
 9. The method of claim 4, wherein assigning a temperature includes: ranking a unit of data as having a lower temperature when the unit of data is written by a host as part of longer data writes.
 10. The method of claim 4, wherein the memory circuit is partitioned into a SLC portion and an MLC portion, where memory cells in the SLC portion each stores one bit of data and memory cells in the MLC portion each stores more than one bit of data, and wherein the SLC portion includes said dedicated portion of the memory array.
 11. The method of claim 10, wherein the SLC portion further includes a non-volatile cache and said set of less than all of the units of data that are more likely to suffer subsequent rewrites due to garbage collection are stored by the memory system in the non-volatile cache.
 12. The method of claim 4, further comprising: in addition to the units of data of said subset, maintaining additional units of data in the dedicated portion of the array, wherein the additional units of data cannot be evicted from the dedicated portion.
 13. The method of claim 4, wherein assigning a temperature includes a ranking units of data biased toward longer term access patterns over shorter term access patterns.
 14. A method of operating on units of data in a non-volatile memory system including a memory circuit having an array organized into blocks of non-volatile memory cells that are erasable together, comprising: determining from among the units of data a set of less than all of the units of data that are more likely to suffer subsequent rewrites due to garbage collection; determining a smaller subset of the units of data from among the units of data of said set that yet more likely to suffer subsequent rewrites due to garbage collection; and maintaining the units of data of said subset in a dedicated portion of the array, wherein determining whether a unit of data is more likely to suffer subsequent rewrites due to garbage collection includes assigning a temperature, where a higher temperature indicates a higher probability that the unit of data will suffer subsequent rewrites due to garbage collection operations, and wherein the memory circuit includes a non-volatile cache section, and wherein the determining of the smaller subset includes: determining whether data written by a host to the non-volatile cache corresponds to one of the units of data of the subset and, if so, writing the data corresponding to said one of the units to the dedicated portion.
 15. The method of claim 14, further comprising: in response to determining that said data written by the host to the non-volatile cache does correspond to one of the units of data of the subset, increasing the temperature assigned to said one of the units of data of the subset unless said assigned temperature already corresponds to a maximum value allowed for the temperatures.
 16. The method of claim 15, further comprising: subsequent to increasing the temperature assigned to said one of the units, determining whether the increased temperature corresponds to the maximum allowed value and, if so, decreasing the temperatures of all other units of data of the subset.
 17. The method of claim 15, further comprising: subsequent to increasing the temperature assigned to said one of the units, determining whether the increased temperature is less than an intermediate temperature value and, if so, setting the increased temperature to equal the intermediate value.
 18. The method of claim 14, further comprising: in response to determining that said data written by the host to the non-volatile cache does not correspond to one of the units of data of the subset, determining whether the memory system is tracking the temperature for said one of the units of data and, if so, increasing the temperature assigned to said one of the units of data of the subset.
 19. The method of claim 18, further comprising: subsequent to increasing the temperature assigned to said one of the units, determining whether the increased temperature exceeds the maximum allowed value and, if so, setting the increased temperature to the maximum allowed value.
 20. The method of claim 18, further comprising: subsequent to increasing the temperature assigned to said one of the units, determining whether the increased temperature is less than an intermediate temperature value and, if so, setting the increased temperature to equal the intermediate value.
 21. The method of claim 18, further comprising: subsequent to increasing the temperature assigned to said one of the units, determining whether the increased temperature corresponds to the maximum allowed value and, if so, decreasing the temperatures of all other units of data of the subset.
 22. The method of claim 21, further comprising: subsequently determining the dedicated portion currently contains a maximum number of allowed units of data and, if not, writing said one of the units to the dedicated portion.
 23. The method of claim 18, further comprising: subsequently determining whether to evict a unit of data from the dedicated portion; and writing said one of the units to the dedicated portion.
 24. The method of claim 18, further comprising: in response to determining that said data written by the host to the non-volatile cache does correspond to one of the units of data of the subset, writing said one of the units to the dedicated portion.
 25. The method of claim 24, further comprising: subsequent to determining whether said data written by the host to the non-volatile cache corresponds to one of the units of data of the subset and prior to said writing said one of the units to the dedicated portion, evicting a unit of data from the dedicated portion. 